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# Copyright (c) OpenMMLab. All rights reserved.
import copy
from typing import Callable, Dict, List, Optional, Union
import numpy as np
from mmcv.transforms import BaseTransform, Compose
from mmcv.transforms.utils import cache_random_params, cache_randomness
from mmdet.registry import TRANSFORMS
@TRANSFORMS.register_module()
class MultiBranch(BaseTransform):
r"""Multiple branch pipeline wrapper.
Generate multiple data-augmented versions of the same image.
`MultiBranch` needs to specify the branch names of all
pipelines of the dataset, perform corresponding data augmentation
for the current branch, and return None for other branches,
which ensures the consistency of return format across
different samples.
Args:
branch_field (list): List of branch names.
branch_pipelines (dict): Dict of different pipeline configs
to be composed.
Examples:
>>> branch_field = ['sup', 'unsup_teacher', 'unsup_student']
>>> sup_pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='LoadAnnotations', with_bbox=True),
>>> dict(type='Resize', scale=(1333, 800), keep_ratio=True),
>>> dict(type='RandomFlip', prob=0.5),
>>> dict(
>>> type='MultiBranch',
>>> branch_field=branch_field,
>>> sup=dict(type='PackDetInputs'))
>>> ]
>>> weak_pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='LoadAnnotations', with_bbox=True),
>>> dict(type='Resize', scale=(1333, 800), keep_ratio=True),
>>> dict(type='RandomFlip', prob=0.0),
>>> dict(
>>> type='MultiBranch',
>>> branch_field=branch_field,
>>> sup=dict(type='PackDetInputs'))
>>> ]
>>> strong_pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='LoadAnnotations', with_bbox=True),
>>> dict(type='Resize', scale=(1333, 800), keep_ratio=True),
>>> dict(type='RandomFlip', prob=1.0),
>>> dict(
>>> type='MultiBranch',
>>> branch_field=branch_field,
>>> sup=dict(type='PackDetInputs'))
>>> ]
>>> unsup_pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='LoadEmptyAnnotations'),
>>> dict(
>>> type='MultiBranch',
>>> branch_field=branch_field,
>>> unsup_teacher=weak_pipeline,
>>> unsup_student=strong_pipeline)
>>> ]
>>> from mmcv.transforms import Compose
>>> sup_branch = Compose(sup_pipeline)
>>> unsup_branch = Compose(unsup_pipeline)
>>> print(sup_branch)
>>> Compose(
>>> LoadImageFromFile(ignore_empty=False, to_float32=False, color_type='color', imdecode_backend='cv2') # noqa
>>> LoadAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, poly2mask=True, imdecode_backend='cv2') # noqa
>>> Resize(scale=(1333, 800), scale_factor=None, keep_ratio=True, clip_object_border=True), backend=cv2), interpolation=bilinear) # noqa
>>> RandomFlip(prob=0.5, direction=horizontal)
>>> MultiBranch(branch_pipelines=['sup'])
>>> )
>>> print(unsup_branch)
>>> Compose(
>>> LoadImageFromFile(ignore_empty=False, to_float32=False, color_type='color', imdecode_backend='cv2') # noqa
>>> LoadEmptyAnnotations(with_bbox=True, with_label=True, with_mask=False, with_seg=False, seg_ignore_label=255) # noqa
>>> MultiBranch(branch_pipelines=['unsup_teacher', 'unsup_student'])
>>> )
"""
def __init__(self, branch_field: List[str],
**branch_pipelines: dict) -> None:
self.branch_field = branch_field
self.branch_pipelines = {
branch: Compose(pipeline)
for branch, pipeline in branch_pipelines.items()
}
def transform(self, results: dict) -> dict:
"""Transform function to apply transforms sequentially.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict:
- 'inputs' (Dict[str, obj:`torch.Tensor`]): The forward data of
models from different branches.
- 'data_sample' (Dict[str,obj:`DetDataSample`]): The annotation
info of the sample from different branches.
"""
multi_results = {}
for branch in self.branch_field:
multi_results[branch] = {'inputs': None, 'data_samples': None}
for branch, pipeline in self.branch_pipelines.items():
branch_results = pipeline(copy.deepcopy(results))
# If one branch pipeline returns None,
# it will sample another data from dataset.
if branch_results is None:
return None
multi_results[branch] = branch_results
format_results = {}
for branch, results in multi_results.items():
for key in results.keys():
if format_results.get(key, None) is None:
format_results[key] = {branch: results[key]}
else:
format_results[key][branch] = results[key]
return format_results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(branch_pipelines={list(self.branch_pipelines.keys())})'
return repr_str
@TRANSFORMS.register_module()
class RandomOrder(Compose):
"""Shuffle the transform Sequence."""
@cache_randomness
def _random_permutation(self):
return np.random.permutation(len(self.transforms))
def transform(self, results: Dict) -> Optional[Dict]:
"""Transform function to apply transforms in random order.
Args:
results (dict): A result dict contains the results to transform.
Returns:
dict or None: Transformed results.
"""
inds = self._random_permutation()
for idx in inds:
t = self.transforms[idx]
results = t(results)
if results is None:
return None
return results
def __repr__(self):
"""Compute the string representation."""
format_string = self.__class__.__name__ + '('
for t in self.transforms:
format_string += f'{t.__class__.__name__}, '
format_string += ')'
return format_string
@TRANSFORMS.register_module()
class ProposalBroadcaster(BaseTransform):
"""A transform wrapper to apply the wrapped transforms to process both
`gt_bboxes` and `proposals` without adding any codes. It will do the
following steps:
1. Scatter the broadcasting targets to a list of inputs of the wrapped
transforms. The type of the list should be list[dict, dict], which
the first is the original inputs, the second is the processing
results that `gt_bboxes` being rewritten by the `proposals`.
2. Apply ``self.transforms``, with same random parameters, which is
sharing with a context manager. The type of the outputs is a
list[dict, dict].
3. Gather the outputs, update the `proposals` in the first item of
the outputs with the `gt_bboxes` in the second .
Args:
transforms (list, optional): Sequence of transform
object or config dict to be wrapped. Defaults to [].
Note: The `TransformBroadcaster` in MMCV can achieve the same operation as
`ProposalBroadcaster`, but need to set more complex parameters.
Examples:
>>> pipeline = [
>>> dict(type='LoadImageFromFile'),
>>> dict(type='LoadProposals', num_max_proposals=2000),
>>> dict(type='LoadAnnotations', with_bbox=True),
>>> dict(
>>> type='ProposalBroadcaster',
>>> transforms=[
>>> dict(type='Resize', scale=(1333, 800),
>>> keep_ratio=True),
>>> dict(type='RandomFlip', prob=0.5),
>>> ]),
>>> dict(type='PackDetInputs')]
"""
def __init__(self, transforms: List[Union[dict, Callable]] = []) -> None:
self.transforms = Compose(transforms)
def transform(self, results: dict) -> dict:
"""Apply wrapped transform functions to process both `gt_bboxes` and
`proposals`.
Args:
results (dict): Result dict from loading pipeline.
Returns:
dict: Updated result dict.
"""
assert results.get('proposals', None) is not None, \
'`proposals` should be in the results, please delete ' \
'`ProposalBroadcaster` in your configs, or check whether ' \
'you have load proposals successfully.'
inputs = self._process_input(results)
outputs = self._apply_transforms(inputs)
outputs = self._process_output(outputs)
return outputs
def _process_input(self, data: dict) -> list:
"""Scatter the broadcasting targets to a list of inputs of the wrapped
transforms.
Args:
data (dict): The original input data.
Returns:
list[dict]: A list of input data.
"""
cp_data = copy.deepcopy(data)
cp_data['gt_bboxes'] = cp_data['proposals']
scatters = [data, cp_data]
return scatters
def _apply_transforms(self, inputs: list) -> list:
"""Apply ``self.transforms``.
Args:
inputs (list[dict, dict]): list of input data.
Returns:
list[dict]: The output of the wrapped pipeline.
"""
assert len(inputs) == 2
ctx = cache_random_params
with ctx(self.transforms):
output_scatters = [self.transforms(_input) for _input in inputs]
return output_scatters
def _process_output(self, output_scatters: list) -> dict:
"""Gathering and renaming data items.
Args:
output_scatters (list[dict, dict]): The output of the wrapped
pipeline.
Returns:
dict: Updated result dict.
"""
assert isinstance(output_scatters, list) and \
isinstance(output_scatters[0], dict) and \
len(output_scatters) == 2
outputs = output_scatters[0]
outputs['proposals'] = output_scatters[1]['gt_bboxes']
return outputs
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